Longfei Li1, Ke Wang2, Xiujian Ma2, Zhenyu Liu3, Shuo Wang4, Jiang Du5, Kaibing Tian2, Xuezhi Zhou6, Wei Wei6, Kai Sun6, Yusong Lin7, Zhen Wu8, Jie Tian9. 1. Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 2. Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100050, China. 3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China. 4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 5. Department of Neuropathology, Beijing Neurosurgical Institute, Beijing, Dongcheng Distract, 100050, China. 6. School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China. 7. Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China; School of Software, Zhengzhou University, Zhengzhou, Henan, 450003, China. Electronic address: yslin@ha.edu.cn. 8. Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100050, China. Electronic address: wuzhen1966@aliyun.com. 9. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China; School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China. Electronic address: jie.tian@ia.ac.cn.
Abstract
PURPOSE: Patients with skull base chordoma and chondrosarcoma have different prognoses and are not readily differentiated preoperatively on imaging. Multiparametric magnetic resonance imaging (MRI) is a routine diagnostic tool that can noninvasively characterize the salient characteristics of tumors. In the present study, we developed and validated a preoperative multiparametric MRI-based radiomic signature for differentiating these tumors. METHOD: This retrospective study enrolled 210 patients and consecutively divided them into the primary and validation cohorts. A total of 1941 radiomic features were acquired from preoperative T1-weighted imaging, T2-weighted imaging and contrast-enhanced T1-weighted imaging for each patient. The most discriminative features were selected by minimum-redundancy maximum-relevancy and recursive feature elimination algorithms in the primary cohort. The multiparametric and single-sequence MRI signatures were constructed with the selected features using a support vector machine model in the primary cohort. The ability of the novel radiomic signatures to differentiate chordoma from chondrosarcoma were assessed using receiver operating characteristic curve analysis in the validation cohort. RESULTS: The multiparametric radiomic signature, which consisted of 11 selected features, reached an area under the receiver operating characteristic curve of 0.9745 and 0.8720 in the primary and validation cohorts, respectively. Moreover, compared with each single-sequence MRI signature, the multiparametric radiomic signature exhibited better classification performance with significant improvement (p < 0.05, Delong's test) in the primary cohorts. CONCLUSION: By combining features from three MRI sequences, the multiparametric radiomics signature can accurately and robustly differentiate skull base chordoma from chondrosarcoma.
PURPOSE:Patients with skull base chordoma and chondrosarcoma have different prognoses and are not readily differentiated preoperatively on imaging. Multiparametric magnetic resonance imaging (MRI) is a routine diagnostic tool that can noninvasively characterize the salient characteristics of tumors. In the present study, we developed and validated a preoperative multiparametric MRI-based radiomic signature for differentiating these tumors. METHOD: This retrospective study enrolled 210 patients and consecutively divided them into the primary and validation cohorts. A total of 1941 radiomic features were acquired from preoperative T1-weighted imaging, T2-weighted imaging and contrast-enhanced T1-weighted imaging for each patient. The most discriminative features were selected by minimum-redundancy maximum-relevancy and recursive feature elimination algorithms in the primary cohort. The multiparametric and single-sequence MRI signatures were constructed with the selected features using a support vector machine model in the primary cohort. The ability of the novel radiomic signatures to differentiate chordoma from chondrosarcoma were assessed using receiver operating characteristic curve analysis in the validation cohort. RESULTS: The multiparametric radiomic signature, which consisted of 11 selected features, reached an area under the receiver operating characteristic curve of 0.9745 and 0.8720 in the primary and validation cohorts, respectively. Moreover, compared with each single-sequence MRI signature, the multiparametric radiomic signature exhibited better classification performance with significant improvement (p < 0.05, Delong's test) in the primary cohorts. CONCLUSION: By combining features from three MRI sequences, the multiparametric radiomics signature can accurately and robustly differentiate skull base chordoma from chondrosarcoma.
Authors: J Bai; J Shi; S Zhang; C Zhang; Y Zhai; S Wang; M Li; C Li; P Zhao; S Geng; S Gui; L Jing; Y Zhang Journal: AJNR Am J Neuroradiol Date: 2020-05-07 Impact factor: 3.825
Authors: Brandon K K Fields; Natalie L Demirjian; Darryl H Hwang; Bino A Varghese; Steven Y Cen; Xiaomeng Lei; Bhushan Desai; Vinay Duddalwar; George R Matcuk Journal: Eur Radiol Date: 2021-04-23 Impact factor: 5.315
Authors: J D Palmer; M E Gamez; K Ranta; H Ruiz-Garcia; J L Peterson; D M Blakaj; D Prevedello; R Carrau; A Mahajan; K L Chaichana; D M Trifiletti Journal: J Neurooncol Date: 2020-08-12 Impact factor: 4.130